SQL-FRONTIER Benchmark Deep-Dive / Temporal & Time-Series / task_005_Q05_iot_sensor_gap_fill_locf
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✔ PASS Temporal & Time-Series task_005_Q05_iot_sensor_gap_fill_locf Scaled: 1M+ Records Ordered: Yes

IoT Sensor Telemetry Gap-Filling via generate_series and LOCF

Candidate Latency (1M Rows)
0.62 ms
(1.6x slower than golden)
Golden Latency (1M Rows)
0.39 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
19.1 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 12.8s
✔ Correctness Verified on 1M+ Records
Candidate SQL output matches ground truth golden query across all multiset rows and numeric tolerances (tol=1e-3). Zero data skew or anti-patterns detected on full dataset scale.

1. Analytical Significance & What It Means

Scenario & Frontier Trap
Business Scenario & Problem

Industrial IoT sensors emit readings intermittently to save bandwidth. Downstream machine learning models require strict continuous 1-minute time grids with Last Observation Carried Forward (LOCF).

⚡ The Frontier Trap (Why LLMs Fail)

Using OUTER JOIN without dynamic range generation leaves temporal voids, leading to missing periods in downstream feature stores.

Prompt / User Request Given to Model
Industrial IoT sensors emit readings intermittently to save bandwidth. Downstream machine learning models require strict continuous 1-minute time grids with Last Observation Carried Forward (LOCF).

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: bucket_time, device_id, raw_reading, filled_temperature.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

1 Tables (1,000,000 rows • 19.1 MiB)

Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:

📊 sensor_telemetry 1,000,000 rows 19.1 MiB Data Volume
device_id (INTEGER) recorded_at (TIMESTAMP) temperature (DOUBLE)
Data Preview (showing sample rows from 1,000,000 total rows):
# device_id recorded_at temperature
1 42 2026-03-01 08:00:00 21.5
2 42 2026-03-01 08:02:00 22
3 42 2026-03-01 08:05:00 22.8
4 42 2026-03-01 09:30:00.000006 22.57
5 43 2026-03-01 09:30:00.000026 21.61
6 44 2026-03-01 09:30:00.000043 22.34
7 45 2026-03-01 09:30:00.000063 21.84
8 46 2026-03-01 09:30:00.000087 21.5
9 47 2026-03-01 09:30:00.000107 21.53
10 48 2026-03-01 09:30:00.000125 21.67
11 49 2026-03-01 09:30:00.000144 22.44
12 50 2026-03-01 09:30:00.000161 22.16
13 51 2026-03-01 09:30:00.000184 21.78
14 52 2026-03-01 09:30:00.000205 22.49
15 53 2026-03-01 09:30:00.000230 22.9

3. Graphical Query Execution Plan & Cost Bottlenecks

Physical Operator Tree (DuckDB 2.0 Profiler)

Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:

Pipeline Latency Distribution (Total Plan Time: 0.62 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
📐 PROJECTION 0.5%
Latency: 0.00 ms
Rows In/Out: 6
Data Vol: 424 B
📁 Projections: ['#0', 'CAST(#1 AS ...
⏳ ORDER_BY 12.5%
Latency: 0.08 ms
Rows In/Out: 6
Data Vol: 406 B
📐 PROJECTION 0.3%
Latency: 0.00 ms
Rows In/Out: 6
Data Vol: 406 B
📁 Projections: ['#0', 'CAST(#1 AS ...
📐 PROJECTION 1.0%
Latency: 0.01 ms
Rows In/Out: 6
Data Vol: 424 B
📁 Projections: ['bucket_time', 'de...
📐 PROJECTION 0.1%
Latency: 0.00 ms
Rows In/Out: 6
Data Vol: 680 B
📁 Projections: ['#0', '#1', '#2', ...
🪟 WINDOW 55.7%
Latency: 0.35 ms
Rows In/Out: 6
Data Vol: 680 B
📁 Projections: last_value(temperat...
📐 PROJECTION 0.4%
Latency: 0.00 ms
Rows In/Out: 6
Data Vol: 632 B
📁 Projections: ['bucket_time', 'de...
⚡ HASH_JOIN 23.7%
Latency: 0.15 ms
Rows In/Out: 6
Data Vol: 608 B
🔍 TABLE_SCAN 3.3%
Latency: 0.02 ms
Rows In/Out: 3
Data Vol: 48 B
📁 memory.main.sensor_telemetry
📐 PROJECTION 0.1%
Latency: 0.00 ms
Rows In/Out: 6
Data Vol: 48 B
📁 Projections: bucket_time
⚙️ UNNEST 2.3%
Latency: 0.01 ms
Rows In/Out: 6
Data Vol: 72 B
🔍 DUMMY_SCAN 0.1%
Latency: 0.00 ms
Rows In/Out: 1
Data Vol: 4 B
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
WINDOW 55.7% 0.35 ms 6 0.00 MB Projections: last_value(temperature IGNORE NULLS Normal
HASH_JOIN 23.7% 0.15 ms 6 0.00 MB - Normal
ORDER_BY 12.5% 0.08 ms 6 0.00 MB - Normal
TABLE_SCAN 3.3% 0.02 ms 3 0.00 MB memory.main.sensor_telemetry Normal
UNNEST 2.3% 0.01 ms 6 0.00 MB - Normal
PROJECTION 1.0% 0.01 ms 6 0.00 MB Projections: ['bucket_time', 'device_id', 'raw_r Normal
PROJECTION 0.5% 0.00 ms 6 0.00 MB Projections: ['#0', 'CAST(#1 AS INTEGER)', '#2', Normal
PROJECTION 0.4% 0.00 ms 6 0.00 MB Projections: ['bucket_time', 'device_id', 'tempe Normal
PROJECTION 0.3% 0.00 ms 6 0.00 MB Projections: ['#0', 'CAST(#1 AS TINYINT)', '#2', Normal
DUMMY_SCAN 0.1% 0.00 ms 1 0.00 MB - Normal
PROJECTION 0.1% 0.00 ms 6 0.00 MB Projections: bucket_time Normal
PROJECTION 0.1% 0.00 ms 6 0.00 MB Projections: ['#0', '#1', '#2', '#3'] Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 0.62 ms) 
WITH time_grid AS
  (SELECT unnest(generate_series(TIMESTAMP '2026-03-01 08:00:00', TIMESTAMP '2026-03-01 08:05:00', INTERVAL '1 minute')) AS bucket_time),
     aligned_readings AS
  (SELECT grid.bucket_time,
          42 AS device_id,
          st.temperature
   FROM time_grid grid
   LEFT JOIN sensor_telemetry st ON grid.bucket_time = st.recorded_at
   AND st.device_id = 42)
SELECT bucket_time,
       device_id,
       temperature AS raw_reading,
       coalesce(temperature, last_value(temperature
                                        IGNORE nulls) OVER (PARTITION BY device_id
                                                            ORDER BY bucket_time ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)) AS filled_temperature
FROM aligned_readings
ORDER BY bucket_time;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 0.39 ms) 
-- Resample sparse telemetry into continuous 1-minute intervals with Last Observation Carried Forward
 WITH time_grid AS
  (SELECT unnest(generate_series(TIMESTAMP '2026-03-01 08:00:00', TIMESTAMP '2026-03-01 08:05:00', INTERVAL '1 minute')) AS bucket_time),
      aligned_readings AS
  (SELECT grid.bucket_time,
          42 AS device_id,
          st.temperature
   FROM time_grid grid
   LEFT JOIN sensor_telemetry st ON grid.bucket_time = st.recorded_at
   AND st.device_id = 42)
SELECT bucket_time,
       device_id,
       temperature AS raw_reading, -- Forward fill the most recent non-null temperature
 coalesce(temperature, last_value(temperature
                                  IGNORE nulls) OVER (PARTITION BY device_id
                                                      ORDER BY bucket_time ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)) AS filled_temperature
FROM aligned_readings
ORDER BY bucket_time;
DuckDB Fixture Setup DDL & Data Population 
CREATE TABLE sensor_telemetry (device_id INT, recorded_at TIMESTAMP, temperature DOUBLE);


INSERT INTO sensor_telemetry
VALUES (42, '2026-03-01 08:00:00', 21.5),
       (42, '2026-03-01 08:02:00', 22.0),
       (42, '2026-03-01 08:05:00', 22.8);

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (6 rows × 4 cols)
# bucket_time device_id raw_reading filled_temperature
1 2026-03-01 08:00:00 42 21.5 21.5
2 2026-03-01 08:01:00 42 NULL 21.5
3 2026-03-01 08:02:00 42 22 22
4 2026-03-01 08:03:00 42 NULL 22
5 2026-03-01 08:04:00 42 NULL 22
6 2026-03-01 08:05:00 42 22.8 22.8
Expected Golden Output (6 rows × 4 cols)
# bucket_time device_id raw_reading filled_temperature
1 2026-03-01 08:00:00 42 21.5 21.5
2 2026-03-01 08:01:00 42 NULL 21.5
3 2026-03-01 08:02:00 42 22 22
4 2026-03-01 08:03:00 42 NULL 22
5 2026-03-01 08:04:00 42 NULL 22
6 2026-03-01 08:05:00 42 22.8 22.8
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